2.6B Model Beats Larger Models: Agent Training Insights
SergioPaniego · x · 2026-08-04
The author shares a 2.6B parameter model that outperforms much larger models on instruction following and tool use. The training process combines techniques like SFT, distillation, and Reinforcement Learning (RL).
Key training stages include:
- MOPD: The student model generates content, and each prompt routes to its domain teacher for token-level feedback.
- Agentic RL: Multi-turn GRPO is used inside real harnesses (e.g., OpenClaw, Hermes Agent) with one sandbox per rollout, while a proxy captures token-level trajectories.
Related event: LFM2.5-2.6B Released: Small Model Beats Larger Counterparts(3 posts)→
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